1. Field of the Invention
The present invention is directed to computer systems; and more particularly, it is directed to digital imaging.
2. Description of the Related Art
Digital images may include raster graphics, vector graphics, or a combination thereof. Raster graphics data (also referred to herein as bitmaps) may be stored and manipulated as a grid of individual picture elements called pixels. A bitmap may be characterized by its width and height in pixels and also by the number of bits per pixel. Commonly, a color bitmap defined in the RGB (red, green blue) color space may comprise between one and eight bits per pixel for each of the red, green, and blue channels. An alpha channel may be used to store additional data such as per-pixel transparency values. Vector graphics data may be stored and manipulated as one or more geometric objects built with geometric primitives. The geometric primitives (e.g., points, lines, polygons, Bézier curves, and text characters) may be based upon mathematical equations to represent parts of digital images.
Digital image processing is the process of analyzing and/or modifying digital images using a computing device, e.g., a computer system. Using specialized software programs, digital images may be manipulated and transformed in a variety of ways. For manipulating and transforming images in a three-dimensional domain, depth information for pixels or objects in the image may be needed. When images are rendered in a three-dimensional domain based on a description of the elements of the image (e.g., geometric primitives), the depth information may be generated along with the color information. However, when images are instead acquired without per-pixel or per-object depth information, it is desirable to estimate such depth information through analysis of the images.
For example, in estimating depth information for a left-eye view and a right-eye view of the same scene (i.e., a stereo pair), prior approaches have typically formulated the problem in terms of finding the global minimum of an appropriate energy function. Global optimization techniques like cooperative optimization, graph cut, and belief propagation have been proposed to minimize the energy function. However, the typical prior approaches have either produced low-quality results or been computationally expensive.